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Research: INFLUENZA Folding Project #18477

Project #18477 overview

Project Summary AI Beta

Miniproteins are tiny proteins being designed to fight diseases like the flu. Scientists want to understand how miniproteins bind to viruses and how to make them even better at fighting infection. They're using computer simulations to study this process in detail, hoping to create more effective miniprotein drugs.
Automated summary; simplified and may not be fully accurate.
Source material

Official Project Description

Designed miniproteins are a class of biomolecules with intermediate sizes—larger than small-molecule drugs, but smaller than monoclonal antibodies.

Miniproteins can be computationally designed to tightly bind protein targets for use as potential therapeutics, a promising new avenue for treating infectious disease. Hemagglutinin is a viral fusion protein that allows H1 influenza A (HA) to bind sialic acid on cell surfaces, as well as being involved in the post-endocytosis mechanism of cellular infection.

The Baker lab at University of Washington has developed de novo designed miniproteins that bind hemagglutinin, and improved their binding through affinity maturation (Chevalier et al.

2017).

Many of the mutations seen in affinity-matured sequences are not found in the binding interface, and it remains an open question how these changes lead to higher affinity.

Furthermore, many of the computational predictions of how single-point mutations affect binding deviate significantly from the experimentally determined values. Could all-atom molecular simulation approaches achieve more accurate predictions? In this set of simulations, we aim to use massively parallel expanded ensemble simulations to predict mutational effects on affinities to hemagglutinin.

By pairing these simulations with other simulations aimed at modeling the binding reactions of these miniproteins to hemagglutinin, we aim to have a relatively complete picture of a miniprotein-target binding reaction and how mutations affect it.

These studies are a large-scale investigation on how miniprotein binding reactions work in atomic detail, towards a better understanding of computational design and modulation of miniprotein therapeutics.

Performance data

Hardware Performance for Project 18477

Compare community-sampled Folding@Home output for the GPUs and CPUs processing this project.

Data as of Sunday, 02 August 2026 21:51:01

CPU PPD Averages Beta

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 EPYC 7B12 64-CORE 64 18,174 1,163,136 AMD
2 RYZEN 9 7950X 16-CORE 32 29,662 949,184 AMD
3 RYZEN 9 7900 12-CORE 24 31,658 759,792 AMD
4 RYZEN 7 7700X 8-CORE 16 37,407 598,512 AMD
5 11TH GEN CORE I7-11700K @ 3.60GHZ 16 26,591 425,456 Intel
6 RYZEN 9 5950X 16-CORE 32 12,470 399,040 AMD
7 RYZEN 7 5800X3D 8-CORE 16 23,767 380,272 AMD
8 RYZEN 7 5700X 8-CORE 16 22,891 366,256 AMD
9 RYZEN 7 5800X 8-CORE 16 20,388 326,208 AMD
10 RYZEN 9 5900X 12-CORE 24 13,069 313,656 AMD
11 XEON PLATINUM 8370C CPU @ 2.80GHZ 16 18,202 291,232 Intel
12 RYZEN 9 3900X 12-CORE 24 11,844 284,256 AMD
13 12TH GEN CORE I7-12700F 20 13,796 275,920 Intel
14 12TH GEN CORE I7-12700 20 13,025 260,500 Intel
15 RYZEN 7 3800X 8-CORE 16 16,182 258,912 AMD
16 CORE I7-10700K CPU @ 3.80GHZ 16 15,088 241,408 Intel
17 RYZEN 7 3700X 8-CORE 16 9,486 151,776 AMD
18 EPYC 7262 8-CORE 16 8,787 140,592 AMD
19 12TH GEN CORE I7-12700H 20 5,514 110,280 Intel
20 CORE I7-10700T CPU @ 2.00GHZ 16 5,589 89,424 Intel
21 XEON CPU L5640 @ 2.27GHZ 24 2,439 58,536 Intel
22 XEON CPU E5-2697 V2 @ 2.70GHZ 24 1,894 45,456 Intel